P088 Amyloid-Induced Network Resilience and Collapse in Alzheimer’s Disease: Insights from Computational Modeling
Ediline L. F. Nguessap*1, Fernando Fagundes Ferreira1
1Department of Physics, University of São Paulo, Ribeirao Preto, Brazil
*Email: fonela@usp.br
Introduction
Alzheimer’s disease (AD) is characterized by progressive synaptic loss, neuronal dysfunction, and network disintegration due to amyloid-beta accumulation [1,2,3,4,5]. While experimental studies identifyamyloid-induced connectivity changes, the role of network resilience; the ability of the brain to maintain function despite synaptic loss remainspoorlyunderstood. Most computational models of AD either focus on static network properties (graph theory-basedapproaches) or single neuron dynamics[6], neglecting the interplay between progressive structural collapse and functional neuronal activity. Here, we model a small-world neuronal network and investigate its structural resilience and dynamical response to amyloid-driven synapse loss.
Methods We construct a small-worldneuronalnetwork with synaptic weights evolving under amyloid-induced weakening. We track network resilience using key metrics: Largest Strongly Connected Component (LSCC) as a measure of global connectivity[7][8]. Global Efficiency, Clustering Coefficient, and Shortest Path Length to quantify functional resilience. To study functional neuronal activity, we simulate a network of Izhikevich neurons with synaptic coupling, observing how firing rates and synchronization evolve before, during, and after LSCC collapse. We further refine our model by removing isolated neurons and reducing background input when LSCC collapses, to ensure biological realism. Results Our simulations reveal a critical amyloid threshold (~75% synaptic loss) beyond which LSCC rapidly collapses, marking the transition from a functionally connected to a fragmented network. Small-world networks exhibit greater resilience than random ones, with LSCC persisting longer due to local clustering and efficient communication pathways. Global efficiency remains stable early on but drops sharply with LSCC collapse, while clustering initially increases (compensatory rewiring) before declining, indicating widespread disconnection. Neuronal firing desynchronizes post-collapse, aligning with cognitive dysfunction in AD, and removing isolated neurons accelerates activity decline, mimicking cortical atrophy. Discussion Our findings suggest that network topology plays a crucial role in Alzheimer’s resilience. As LSCC shrinks past a critical threshold, functional decline accelerates, aligning with AD progression. Neurons remain active but lose synchronization, suggesting that cortical regions stay active in late AD stages but fail to coordinate information transfer. Biologically inspired modifications (removing isolated neurons, reducing background input) enhance realism by preventing unrealistic activity after connectivity loss. This suggests that network vulnerability could serve as an AD biomarker. Future work should explore synaptic plasticity, tau pathology, and patient data (EEG, fMRI) for furtherimprovement.
Acknowledgements FFF is supported by Brazilian National Council for Scientific and Technological Development (CNPq) 316664/2021-9. ELFN is supported by Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES). References
